SoroushVahidi/scidocs huggingface dataset:
A robust fault modeling and prediction technique for heterogeneous network
(2017)

This paper proposes a modelling technique for fault identification in a heterogeneous network. Heterogeneous network is the integration of wired network, cellular network, wireless local area network (WLAN), and Adhoc network, etc. There may be several numbers of causes and sub-causes of fault in each type of network. These causes of fault must be mitigated in order to have a secure and reliable system. The paper introduces a model based approach that starts with the enumeration of possible causes of each fault over the heterogeneous network. A framework is then created by such causes which diagrammatically describe the causes and sub-causes responsible for the occurrence of a fault. This paper suggests a conceptual fault cause tree model based on the probabilistic framework for ranking the faults and their possible causes. An effective mitigation strategy is required to mitigate the causes and sub-causes. Once mitigation of the cause creating a fault is achieved, the system is analyzed and tested for accuracy and reliability. The Proposed technique assures that all sub-causes even at the lowest level of abstraction is taken into consideration in making the system more robust against the particular fault. The proposed model is also used to analyze the faulty probability of a heterogeneous network.
One-against-all multi-class SVM classification using reliability measures
(2005)

Support vector machines (SVM) is originally designed for binary classification. To extend it to multi-class scenario, a typical conventional way is to decompose an M-class problem into a series of two-class problems, for which one-against-all is the earliest and one of the most widely used implementations. However, certain theoretical analysis reveals a drawback, i.e., the competence of each classifier is totally neglected when the results of classification from the multiple classifiers are combined for the final decision. To overcome this limitation, this paper introduces reliability measures into the multi-class framework. Two measures are designed: static reliability measure (SRM) and dynamic reliability measure (DRM). SRM works on a collective basis and yields a constant value regardless of the location of the test sample. DRM, on the other hand, accounts for the spatial variation of the classifier's performance. Based on these two reliability measures, a new decision strategy for the one-against-all method is proposed, which is tested on benchmark data sets and demonstrates its effectiveness.
Motion Capture and Retargeting of Fish by Monocular Camera
(2017)

Accurate motion capture and flexible retargeting of underwater creatures such as fish remain to be difficult due to the long-lasting challenges of marker attachment and feature description for soft bodies in the underwater environment. Despite limited new research progresses appeared in recent years, the fish motion retargeting with a desirable motion pattern in real-time remains elusive. Strongly motivated by our ambitious goal of achieving high-quality data-driven fish animation with a light-weight, mobile device, this paper develops a novel framework of motion capturing and retargeting for a fish. We capture the motion of actual fish by a monocular camera without the utility of any marker. The elliptical Fourier coefficients are then integrated into the contour-based feature extraction process to analyze the fish swimming patterns. This novel approach can obtain the motion information in a robust way, with smooth medial axis as the descriptor for a soft fish body. For motion retargeting, we propose a two-level scheme to properly transfer the captured motion into new models, such as 2D meshes (with texture) generated from pictures or 3D models designed by artists, regardless of different body geometry and fin proportions among various species. Both motion capture and retargeting processes are functioning in real time. Hence, the system can simultaneously create fish animation with variation, while obtaining video sequences of real fish by a monocular camera.
Joint shot boundary detection and key frame extraction
(2012)

Representing a video by a set of key frames is useful for efficient video browsing and retrieving. But key frame extraction keeps a challenge in the computer vision field. In this paper, we propose a joint framework to integrate both shot boundary detection and key frame extraction, wherein three probabilistic components are taken into account, i.e. the prior of the key frames, the conditional probability of shot boundaries and the conditional probability of each video frame. Thus the key frame extraction is treated as a Maximum A Posteriori which can be solved by adopting alternate strategy. Experimental results show that the proposed method preserves the scene level structure and extracts key frames that are representative and discriminative.
Design and analysis of cross-fed rectangular array antenna; an X-band microstrip array antenna, operating at 11 GHz
(2016)

In this paper, an X-Band Microstrip Patch antenna termed as the Cross fed rectangular array antenna has been presented with an enhanced radiation efficiency. The proposed antenna is designed and simulated using FEKO 5.5 suite. It is then fabricated on a 40 × 40mm2 Rogers RT-Duroid 5880LZ dielectric material board. This antenna is composed of a rectangular patch array in a cross fashion with four patches, each with a dimension of 12.3 mm × 9.85 mm excited using a wire-port. The antenna operates at a frequency of 11.458 GHz in the X-Band (8-12 GHz). It has achieved stable radiation efficiency of 80.71% with a gain of 7.4 dB at the operating frequency. It is thus inferred that this antenna can be used for X-Band applications such as in maritime navigation and airborne radars.
Deep Cross-Modal Projection Learning for Image-Text Matching
(2018)

The key point of image-text matching is how to accurately measure the similarity between visual and textual inputs. Despite the great progress of associating the deep cross-modal embeddings with the bi-directional ranking loss, developing the strategies for mining useful triplets and selecting appropriate margins remains a challenge in real applications. In this paper, we propose a cross-modal projection matching (CMPM) loss and a cross-modal projection classification (CMPC) loss for learning discriminative image-text embeddings. The CMPM loss minimizes the KL divergence between the projection compatibility distributions and the normalized matching distributions defined with all the positive and negative samples in a mini-batch. The CMPC loss attempts to categorize the vector projection of representations from one modality onto another with the improved norm-softmax loss, for further enhancing the feature compactness of each class. Extensive analysis and experiments on multiple datasets demonstrate the superiority of the proposed approach.
Efficient Ordered Combinatorial Semi-Bandits for Whole-Page Recommendation
(2017)

Multi-Armed Bandit (MAB) framework has been successfully applied in many web applications. However, many complex real-world applications that involve multiple content recommendations cannot fit into the traditional MAB setting. To address this issue, we consider an ordered combinatorial semi-bandit problem where the learner recommends S actions from a base set of K actions, and displays the results in S (out of M ) different positions. The aim is to maximize the cumulative reward with respect to the best possible subset and positions in hindsight. By the adaptation of a minimumcost maximum-flow network, a practical algorithm based on Thompson sampling is derived for the (contextual) combinatorial problem, thus resolving the problem of computational intractability. With its potential to work with whole-page recommendation and any probabilistic models, to illustrate the effectiveness of our method, we focus on Gaussian process optimization and a contextual setting where click-throughrate is predicted using logistic regression. We demonstrate the algorithms’ performance on synthetic Gaussian process problems and on large-scale news article recommendation datasets from Yahoo! Front Page Today Module.
Overweight among primary school-age children in Malaysia.
(2013)

This study is a secondary data analysis from the National Health Morbidity Survey III, a population-based study conducted in 2006. A total of 7,749 children between 7 and 12 years old were recruited into the study. This study seeks to report the prevalence of overweight (including obesity) children in Malaysia using international cut-off point and identify its associated key social determinants. The results show that the overall prevalence of overweight children in Malaysia was 19.9%. The urban residents, males, Chinese, those who are wealthy, have overweight or educated guardians showed higher prevalence of overweight. In multivariable analysis, higher likelihood of being overweight was observed among those with advancing age (OR=1.15), urban residents (OR=1.16, 95% CI: 1.01-1.36), the Chinese (OR=1.45, 95% CI: 1.19-1.77), boys (OR=1.23, 95% CI: 1.08-1.41), and those who came from higher income family. In conclusion, one out of five of 7-12 year-old-children in Malaysia were overweight. Locality of residence, ethnicity, gender, guardian education, and overweight guardian were likely to be the predictors of this alarming issue. Societal and public health efforts are needed in order to reduce the burden of disease associated with obesity.
Mesh Location in Open Ventral Hernia Repair: A Systematic Review and Network Meta-analysis
(2015)

There is no consensus on the ideal location for mesh placement in open ventral hernia repair (OVHR). We aim to identify the mesh location associated with the lowest rate of recurrence following OVHR using a systematic review and meta-analysis. A search was performed for studies comparing at least two of four locations for mesh placement during OVHR (onlay, inlay, sublay, and underlay). Outcomes assessed were hernia recurrence and surgical site infection (SSI). Pairwise meta-analysis was performed to compare all direct treatment of mesh locations. A multiple treatment meta-analysis was performed to compare all mesh locations in the Bayesian framework. Sensitivity analyses were planned for the following: studies with a low risk of bias, incisional hernias, by hernia size, and by mesh type (synthetic or biologic). Twenty-one studies were identified (n = 5,891). Sublay placement of mesh was associated with the lowest risk for recurrence [OR 0.218 (95 % CI 0.06–0.47)] and was the best of the four treatment modalities assessed [Prob (best) = 94.2 %]. Sublay was also associated with the lowest risk for SSI [OR 0.449 (95 % CI 0.12–1.16)] and was the best of the 4 treatment modalities assessed [Prob (best) = 77.3 %]. When only assessing studies at low risk of bias, of incisional hernias, and using synthetic mesh, the probability that sublay had the lowest rate of recurrence and SSI was high. Sublay mesh location has lower complication rates than other mesh locations. While additional randomized controlled trials are needed to validate these findings, this network meta-analysis suggests the probability of sublay being the best location for mesh placement is high.
Learned Step Size Quantization
(2019)

We present here Learned Step Size Quantization, a method for training deep networks such that they can run at inference time using low precision integer matrix multipliers, which offer power and space advantages over high precision alternatives. The essence of our approach is to learn the step size parameter of a uniform quantizer by backpropagation of the training loss, applying a scaling factor to its learning rate, and computing its associated loss gradient by ignoring the discontinuity present in the quantizer. This quantization approach can be applied to activations or weights, using different levels of precision as needed for a given system, and requiring only a simple modification of existing training code. As demonstrated on the ImageNet dataset, our approach achieves better accuracy than all previous published methods for creating quantized networks on several ResNet network architectures at 2-, 3and 4-bits of precision.
Design of EMG Acquisition Circuit to Control an Antagonistic Mechanism Actuated by Pneumatic Artificial Muscles PAMs
(2017)

-A pneumatically actuated antagonistic pair of muscles with joint mechanism (APMM) is supported and developed to be essential for bionic and biomimetic applications to emulate the biological muscles by realizing various kinds of locomotion based on normal electrical activity of biological muscles. This Paper aims to compare the response of antagonistic pairs of muscles mechanism (APMM) based on the pneumatic artificial muscles (PAMs) to an EMG signal that was acquired throw a designed circuit and an EMG Laboratory acquisition kit. The response is represented as a joint rotary displacement generated by the contraction and extension of the pneumatic artificial muscles. A statistical study was done to prove the efficiency of the designed circuit the response of antagonistic pairs of muscles mechanism. The statistical result showed that there is no significant difference of voltage data in both EMG acquired signal between reference kit and designed circuit. An excellent correlation behavior between the EMG control signal and the response of APMM as an angular displacement has been discussed and statistically analyzed. Index Term-Pneumatic Artificial Muscles, Biomechatronics, Electromyogram EMG, Pneumatic Proportional Directional Control Valve, Signal Processing, Bionic.
Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles
(2007)

We present a data-driven variant of the LR algorithm for dependency parsing, and extend it with a best-first search for probabilistic generalized LR dependency parsing. Parser actions are determined by a classifier, based on features that represent the current state of the parser. We apply this parsing framework to both tracks of the CoNLL 2007 shared task, in each case taking advantage of multiple models trained with different learners. In the multilingual track, we train three LR models for each of the ten languages, and combine the analyses obtained with each individual model with a maximum spanning tree voting scheme. In the domain adaptation track, we use two models to parse unlabeled data in the target domain to supplement the labeled out-ofdomain training set, in a scheme similar to one iteration of co-training.
Analysis of Blockage Effects on Urban Cellular Networks
(2014)

Large-scale blockages such as buildings affect the performance of urban cellular networks, especially at higher frequencies. Unfortunately, such blockage effects are either neglected or characterized by oversimplified models in the analysis of cellular networks. Leveraging concepts from random shape theory, this paper proposes a mathematical framework to model random blockages and analyze their impact on cellular network performance. Random buildings are modeled as a process of rectangles with random sizes and orientations whose centers form a Poisson point process on the plane. The distribution of the number of blockages in a link is proven to be a Poisson random variable with parameter dependent on the length of the link. Our analysis shows that the probability that a link is not intersected by any blockages decays exponentially with the link length. A path loss model that incorporates the blockage effects is also proposed, which matches experimental trends observed in prior work. The model is applied to analyze the performance of cellular networks in urban areas with the presence of buildings, in terms of connectivity, coverage probability, and average rate. Our results show that the base station density should scale superlinearly with the blockage density to maintain the network connectivity. Our analyses also show that while buildings may block the desired signal, they may still have a positive impact on the SIR coverage probability and achievable rate since they can block significantly more interference.
Depth enhanced visual-inertial odometry based on Multi-State Constraint Kalman Filter
(2017)

There have been increasing demands for developing robotic system combining camera and inertial measurement unit in navigation task, due to their low-cost, lightweight and complementary properties. In this paper, we present a Visual Inertial Odometry (VIO) system which can utilize sparse depth to estimate 6D pose in GPS-denied and unstructured environments. The system is based on Multi-State Constraint Kalman Filter (MSCKF), which benefits from low computation load when compared to optimization-based method, especially on resource-constrained platform. Features are enhanced with depth information forming 3D landmark position measurements in space, which reduces uncertainty of position estimate. And we derivate measurement model to access compatibility with both 2D and 3D measurements. In experiments, we evaluate the performance of the system in different in-flight scenarios, both cluttered room and industry environment. The results suggest that the estimator is consistent, substantially improves the accuracy compared with original monocular-based MSKCF and achieves competitive accuracy with other research.
Web scraping technologies in an API world
(2014)

Web services are the de facto standard in biomedical data integration. However, there are data integration scenarios that cannot be fully covered by Web services. A number of Web databases and tools do not support Web services, and existing Web services do not cover for all possible user data demands. As a consequence, Web data scraping, one of the oldest techniques for extracting Web contents, is still in position to offer a valid and valuable service to a wide range of bioinformatics applications, ranging from simple extraction robots to online meta-servers. This article reviews existing scraping frameworks and tools, identifying their strengths and limitations in terms of extraction capabilities. The main focus is set on showing how straightforward it is today to set up a data scraping pipeline, with minimal programming effort, and answer a number of practical needs. For exemplification purposes, we introduce a biomedical data extraction scenario where the desired data sources, well-known in clinical microbiology and similar domains, do not offer programmatic interfaces yet. Moreover, we describe the operation of WhichGenes and PathJam, two bioinformatics meta-servers that use scraping as means to cope with gene set enrichment analysis.
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
(2015)

To describe the log-likelihood computation in our model, let us consider a two scale pyramid for the moment. Given a (vectorized) j × j image I , denote by l = d(I) the coarsened image, and h = I − u(d(I)) to be the high pass. In this section, to simplify the computations, we use a slightly different u operator than the one used to generate the images displayed in Figure 3 of the paper. Namely, here we take d(I) to be the mean over each disjoint block of 2× 2 pixels, and take u to be the operator that removes the mean from each 2× 2 block. Since u has rank 3d/4, in this section, we write h in an orthonormal basis of the range of u, then the (linear) mapping from I to (l, h) is unitary. We now build a probability density p on Rd2 by p(I) = q0(l, h)q1(l) = q0(d(I), h(I))q1(d(I)); in a moment we will carefully define the functions qi. For now, suppose that qi ≥ 0, ∫ q1(l) dl = 1, and for each fixed l, ∫ q0(l, h) dh = 1. Then we can check that p has unit integral: ∫
Show and tell: A neural image caption generator
(2015)

Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art.
Generative Adversarial Networks
(2014)

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1 2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
Rules and Ontologies for the Semantic Web
(2008)

Rules and ontologies play a key role in the layered architecture of the Semantic Web, as they are used to ascribe meaning to, and to reason about, data on the Web. While the Ontology Layer of the Semantic Web is quite developed, and the Web Ontology Language (OWL) is a W3C recommendation since a couple of years already, the rules layer is far less developed and an active area of research; a number of initiatives and proposals have been made so far, but no standard as been released yet. Many implementations of rule engines are around which deal with Semantic Web data in one or another way. This article gives a comprehensive, although not exhaustive, overview of such systems, describes their supported languages, and sets them in relation with theoretical approaches for combining rules and ontologies as foreseen in the Semantic Web architecture. In the course of this, we identify desired properties and common features of rule languages and evaluate existing systems against their support. Furthermore, we review technical problems underlying the integration of rules and ontologies, and classify representative proposals for theoretical integration approaches into different categories.
Process Mining in IT Service Management: A Case Study
(2016)

The explosion of process-related data in nowadays organizations has raised the interest to exploit the data in order to know in deep how the business processes are being carried out. To face this need, process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from process related data. In this paper, we present a case study of process mining in a real IT service management scenario. We describe an exploratory analysis of real life event logs generated between 2012 and 2015, in four different processes designed within an IT platform. More specifically, we analyze the way of handling the different requests and incidents registered in an organization.